Abstract
We present a monolingual alignment system for long, sentence- or clause-level alignments, and demonstrate that systems designed for word- or short phrase-based alignment are ill-suited for these longer alignments. Our system is capable of aligning semantically similar spans of arbitrary length. We achieve significantly higher recall on aligning phrases of four or more words and outperform state-of-the- art aligners on the long alignments in the MSR RTE corpus.- Anthology ID:
- P19-1467
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4724–4735
- Language:
- URL:
- https://aclanthology.org/P19-1467
- DOI:
- 10.18653/v1/P19-1467
- Cite (ACL):
- Jessica Ouyang and Kathy McKeown. 2019. Neural Network Alignment for Sentential Paraphrases. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4724–4735, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Neural Network Alignment for Sentential Paraphrases (Ouyang & McKeown, ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1467.pdf